The performance of a 3-D forward-looking bottom mapping and obstacle detection sonar can be limited by the beamwidth of the receive sensor array. Most phase array technologies utilize a Fourier based beamformer to transfer the received signals from the individual channel domain to a direction angle domain.
A Fourier-based beamformer has a beamwidth in the x or y direction of approximately 50/M degrees where M is the size of the array in the x or y axis as measured in wavelengths. When these closely spaced signals (134) fall within a beamwidth of one another and near each other in time, a Fourier-based beamformer is unable to separate the two or more signals. Additionally, Fourier based beamformers create signal artifacts called sidelobes. Sidelobes generated by the beamformer mathematics are signal which appear to be targets with a small signal level coming from directions where there are no targets. These sidelobes can be falsely detected as actual targets.
An alternative to Fourier based beamformers is the model-based beamformer. A model-based beamformer exploits a priori knowledge of the signal such as the fact that it is a plane wave with a certain direction. In a two-dimensional problem with N sensors (hydrophones in the preferred embodiment), the Fourier based beamformer calculates the N or more power levels associated with each direction. In the model-based beamformer, the unknown directions can be the subject of a search or optimization. The phase or time relationship between each of the sensors is represented in the covariance matrix. This complex matrix is the expected value of the product of the sensor outputs. The phases of the off-diagonal terms contain the information about the direction of the signal. The preferred embodiment of the invention involves exploiting the covariance structure of the received signals on the array.
Non-Fourier based beamformers have been developed over the past few decades. These approaches offer the promise of higher resolutions for a given array aperture and reduced sidelobe levels. This capability offers the advantage of improved target separation and the detection of small targets at the same absolute range as larger targets without the false alarm rate of detecting the larger target's sidelobes as actual smaller targets. However, there techniques have been developed for passive systems. When applying them to active systems, there are two common limitations, with most non-Fourier based beamformers having one or more of these limitations.
One limitation is they require a statistically relevant number of samples from the same signal. These are often called “snapshots”. In an active system, the signals created by the targets are generally of long or continuous duration such as the noise created “whirl” of a propeller or the “roar” of an engine. When applied to an active system, these solutions still require multiple snapshots. If the sonar system and all targets where completely stationary and the environment was static, multiple transmissions could be made on the same target “scene” with each transmission being a single snapshot. In the practical implementation of an active sonar system, this is generally not possible. For obstacle avoidance sonar systems, the either sonar system or the target, or both are moving and the scene is not stationary. Therefore, approaches demonstrated in the prior art by such non-Fourier based beamformers requiring multiple snapshots are not practical for forward-looking sonar systems.
The second limitation is that they require a priori information about the number of targets they should detect. Under most situations, this requirement is not practical. In unknown environments such as those encountered where active sonar systems are used, it is generally impossible to know how many targets are to be detected at a given range.